Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Feature exploration for biometric recognition using millimetre wave body images

Abstract: The use of millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. Furthermore, the security community has started using millimetre wave screening scanners in order to detect concealed objects. We believe we can exploit the use of these devices by incorporating biometric functionalities. This paper proposes a biometric recognition system based on the information of the silhouette of the human body, which may be seen as a type of soft biometric trait. To this aim, we report experimental results on the BIOGIGA database with four feature extraction approaches (contour coordinates, shape contexts, Fourier descriptors and landmarks) and three classification methods (Euclidean distance, dynamic time warping and support vector machines). The best configuration of 1.33 % EER is achieved when using contour coordinates with dynamic time warping.

Determination of the number of writing variants with an HMM based cursive word recognition system

Abstract: An important parameter for building a cursive script model is the number of different, relevant letter writing variants. An algorithm performing this task automatically by optimizing the number of letter models in an HMM-based script recognition system is presented. The algorithm iteratively modified selected letter models; for selection, quality measures like HMM distance and emission weight entropy are developed, and their correlation with recognition performance is shown. Theoretical measures for the selection of overall model complexity are presented, but best results are obtained by direct selection criteria: likelihood and recognition rate of training data. With the optimized models, an average improvement in recognition rate of up to 5.8 percent could be achieved.

Isolated word intonation recognition using hidden Markov models

Abstract: A method is described for recognition of intonation patterns based on discrete distribution hidden Markov models (HMMs) and vector quantization techniques. Fundamental frequency and energy features, were used to determine the best combination of feature processing and quantization techniques for recognition of statement, question, command, calling, and continuation intonation patterns in isolated words. A recognition accuracy of 89% was achieved for the best-case speaker- and word-independent performance. Recognition performance of human listeners on a 100-word subset yielded 77% accuracy, compared to 83% using HMMs on the same subset. >

An improved Artificial Immune Recognition System for off-line handwritten signature verification

Abstract: This paper introduces an improved implementation of Artificial Immune Recognition System (AIRS) to solve the automatic off-line handwritten signature verification. Conventionally, the AIRS training provide a set of memory cells that are used with a k-Nearest Neighbors decision to classify test patterns. In order to improve the verification ability, we propose to substitute the k-NN classification by a trainable decision function using SVM classifier. In addition, for signature characterization, new gradient local binary pattern features are introduced. Experiments are conducted on CEDAR and GPDS-300 corpuses. The results show that the proposed algorithm overcomes the conventional AIRS-kNN by more than 9% in the average error rate. Also, it gives similar and sometimes better performance than the state of the art.

Offline handwritten text recognition using support vector machines

Abstract: Comenia script is a novel handwritten text introduced at primary schools in the Czech Republic This paper describes a method for handwritten text recognition (HWR) of this font In particular it proposes a method for preprocessing and normalization of data and optical character recognition based on SVM classifier We have trained and statistically evaluated several models, where we have focused on recognition of different styles of writing of the same characters — for the forensic purposes and identification of the author of a document The best model has achieved 9286 % accuracy without any further postprocessing, eg a spellchecker We also proposed using more than one classification model for character recognition that has shown to increase accuracy when compared to a single model approach